/*
 *    This program is free software; you can redistribute it and/or modify
 *    it under the terms of the GNU General Public License as published by
 *    the Free Software Foundation; either version 2 of the License, or
 *    (at your option) any later version.
 *
 *    This program is distributed in the hope that it will be useful,
 *    but WITHOUT ANY WARRANTY; without even the implied warranty of
 *    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 *    GNU General Public License for more details.
 *
 *    You should have received a copy of the GNU General Public License
 *    along with this program; if not, write to the Free Software
 *    Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA.
 */

/*
 *    SimpleKMeans.java
 *    Copyright (C) 2000 University of Waikato, Hamilton, New Zealand
 *
 */
package weka.clusterers;

import java.util.Enumeration;
import java.util.HashMap;
import java.util.Random;
import java.util.Vector;

import weka.classifiers.rules.DecisionTableHashKey;
import weka.core.Attribute;
import weka.core.Capabilities;
import weka.core.Capabilities.Capability;
import weka.core.DistanceFunction;
import weka.core.EuclideanDistance;
import weka.core.Instance;
import weka.core.Instances;
import weka.core.ManhattanDistance;
import weka.core.Option;
import weka.core.RevisionUtils;
import weka.core.Utils;
import weka.core.WeightedInstancesHandler;
import weka.filters.Filter;
import weka.filters.unsupervised.attribute.ReplaceMissingValues;

/**
 * <!-- globalinfo-start --> Cluster data using the k means algorithm
 * <p/>
 * <!-- globalinfo-end -->
 * 
 * <!-- options-start --> Valid options are:
 * <p/>
 * 
 * <pre>
 * -N &lt;num&gt;
 *  number of clusters.
 *  (default 2).
 * </pre>
 * 
 * <pre>
 * -V
 *  Display std. deviations for centroids.
 * </pre>
 * 
 * <pre>
 * -M
 *  Replace missing values with mean/mode.
 * </pre>
 * 
 * <pre>
 * -S &lt;num&gt;
 *  Random number seed.
 *  (default 10)
 * </pre>
 * 
 * <pre>
 * -A &lt;classname and options&gt;
 *  Distance function to be used for instance comparison
 *  (default weka.core.EuclidianDistance)
 * </pre>
 * 
 * <pre>
 * -I &lt;num&gt;
 *  Maximum number of iterations.
 * </pre>
 * 
 * <pre>
 * -O 
 *  Preserve order of instances.
 * </pre>
 * 
 * 
 * <!-- options-end -->
 * 
 * @author Mark Hall (mhall@cs.waikato.ac.nz)
 * @author Eibe Frank (eibe@cs.waikato.ac.nz)
 * @version $Revision: 10537 $
 * @see RandomizableClusterer
 */
public class SimpleKMeans extends RandomizableClusterer implements
  NumberOfClustersRequestable, WeightedInstancesHandler {

  /** for serialization */
  static final long serialVersionUID = -3235809600124455376L;

  /**
   * replace missing values in training instances
   */
  private ReplaceMissingValues m_ReplaceMissingFilter;

  /**
   * number of clusters to generate
   */
  private int m_NumClusters = 2;

  /**
   * holds the cluster centroids
   */
  private Instances m_ClusterCentroids;

  /**
   * Holds the standard deviations of the numeric attributes in each cluster
   */
  private Instances m_ClusterStdDevs;

  /**
   * For each cluster, holds the frequency counts for the values of each nominal
   * attribute
   */
  private int[][][] m_ClusterNominalCounts;
  private int[][] m_ClusterMissingCounts;

  /**
   * Stats on the full data set for comparison purposes In case the attribute is
   * numeric the value is the mean if is being used the Euclidian distance or
   * the median if Manhattan distance and if the attribute is nominal then it's
   * mode is saved
   */
  private double[] m_FullMeansOrMediansOrModes;
  private double[] m_FullStdDevs;
  private int[][] m_FullNominalCounts;
  private int[] m_FullMissingCounts;

  /**
   * Display standard deviations for numeric atts
   */
  private boolean m_displayStdDevs;

  /**
   * Replace missing values globally?
   */
  private boolean m_dontReplaceMissing = false;

  /**
   * The number of instances in each cluster
   */
  private int[] m_ClusterSizes;

  /**
   * Maximum number of iterations to be executed
   */
  private int m_MaxIterations = 500;

  /**
   * Keep track of the number of iterations completed before convergence
   */
  private int m_Iterations = 0;

  /**
   * Holds the squared errors for all clusters
   */
  private double[] m_squaredErrors;

  /** the distance function used. */
  protected DistanceFunction m_DistanceFunction = new EuclideanDistance();

  /**
   * Preserve order of instances
   */
  private boolean m_PreserveOrder = false;

  /**
   * Assignments obtained
   */
  protected int[] m_Assignments = null;

  /**
   * the default constructor
   */
  public SimpleKMeans() {
    super();

    m_SeedDefault = 10;
    setSeed(m_SeedDefault);
  }

  /**
   * Returns a string describing this clusterer
   * 
   * @return a description of the evaluator suitable for displaying in the
   *         explorer/experimenter gui
   */
  public String globalInfo() {
    return "Cluster data using the k means algorithm. Can use either "
      + "the Euclidean distance (default) or the Manhattan distance."
      + " If the Manhattan distance is used, then centroids are computed "
      + "as the component-wise median rather than mean.";
  }

  /**
   * Returns default capabilities of the clusterer.
   * 
   * @return the capabilities of this clusterer
   */
  
  public Capabilities getCapabilities() {
    Capabilities result = super.getCapabilities();
    result.disableAll();
    result.enable(Capability.NO_CLASS);

    // attributes
    result.enable(Capability.NOMINAL_ATTRIBUTES);
    result.enable(Capability.NUMERIC_ATTRIBUTES);
    result.enable(Capability.MISSING_VALUES);

    return result;
  }

  /**
   * Generates a clusterer. Has to initialize all fields of the clusterer that
   * are not being set via options.
   * 
   * @param data set of instances serving as training data
   * @throws Exception if the clusterer has not been generated successfully
   */
  
  public void buildClusterer(Instances data) throws Exception {

    // can clusterer handle the data?
    getCapabilities().testWithFail(data);

    m_Iterations = 0;

    m_ReplaceMissingFilter = new ReplaceMissingValues();
    Instances instances = new Instances(data);

    instances.setClassIndex(-1);
    if (!m_dontReplaceMissing) {
      m_ReplaceMissingFilter.setInputFormat(instances);
      instances = Filter.useFilter(instances, m_ReplaceMissingFilter);
    }

    m_FullMissingCounts = new int[instances.numAttributes()];
    if (m_displayStdDevs) {
      m_FullStdDevs = new double[instances.numAttributes()];
    }
    m_FullNominalCounts = new int[instances.numAttributes()][0];

    m_FullMeansOrMediansOrModes = moveCentroid(0, instances, false);
    for (int i = 0; i < instances.numAttributes(); i++) {
      m_FullMissingCounts[i] = instances.attributeStats(i).missingCount;
      if (instances.attribute(i).isNumeric()) {
        if (m_displayStdDevs) {
          m_FullStdDevs[i] = Math.sqrt(instances.variance(i));
        }
        if (m_FullMissingCounts[i] == instances.numInstances()) {
          m_FullMeansOrMediansOrModes[i] = Double.NaN; // mark missing as mean
        }
      } else {
        m_FullNominalCounts[i] = instances.attributeStats(i).nominalCounts;
        if (m_FullMissingCounts[i] > m_FullNominalCounts[i][Utils
          .maxIndex(m_FullNominalCounts[i])]) {
          m_FullMeansOrMediansOrModes[i] = -1; // mark missing as most common
                                               // value
        }
      }
    }

    m_ClusterCentroids = new Instances(instances, m_NumClusters);
    int[] clusterAssignments = new int[instances.numInstances()];

    if (m_PreserveOrder) {
      m_Assignments = clusterAssignments;
    }

    m_DistanceFunction.setInstances(instances);

    Random RandomO = new Random(getSeed());
    int instIndex;
    HashMap initC = new HashMap();
    DecisionTableHashKey hk = null;

    Instances initInstances = null;
    if (m_PreserveOrder) {
      initInstances = new Instances(instances);
    } else {
      initInstances = instances;
    }

    for (int j = initInstances.numInstances() - 1; j >= 0; j--) {
      instIndex = RandomO.nextInt(j + 1);
      hk = new DecisionTableHashKey(initInstances.instance(instIndex),
        initInstances.numAttributes(), true);
      if (!initC.containsKey(hk)) {
        m_ClusterCentroids.add(initInstances.instance(instIndex));
        initC.put(hk, null);
      }
      initInstances.swap(j, instIndex);

      if (m_ClusterCentroids.numInstances() == m_NumClusters) {
        break;
      }
    }

    m_NumClusters = m_ClusterCentroids.numInstances();

    // removing reference
    initInstances = null;

    int i;
    boolean converged = false;
    int emptyClusterCount;
    Instances[] tempI = new Instances[m_NumClusters];
    m_squaredErrors = new double[m_NumClusters];
    m_ClusterNominalCounts = new int[m_NumClusters][instances.numAttributes()][0];
    m_ClusterMissingCounts = new int[m_NumClusters][instances.numAttributes()];
    while (!converged) {
      emptyClusterCount = 0;
      m_Iterations++;
      converged = true;
      for (i = 0; i < instances.numInstances(); i++) {
        Instance toCluster = instances.instance(i);
        int newC = clusterProcessedInstance(toCluster, true);
        if (newC != clusterAssignments[i]) {
          converged = false;
        }
        clusterAssignments[i] = newC;
      }

      // update centroids
      m_ClusterCentroids = new Instances(instances, m_NumClusters);
      for (i = 0; i < m_NumClusters; i++) {
        tempI[i] = new Instances(instances, 0);
      }
      for (i = 0; i < instances.numInstances(); i++) {
        tempI[clusterAssignments[i]].add(instances.instance(i));
      }
      for (i = 0; i < m_NumClusters; i++) {
        if (tempI[i].numInstances() == 0) {
          // empty cluster
          emptyClusterCount++;
        } else {
          moveCentroid(i, tempI[i], true);
        }
      }

      if (m_Iterations == m_MaxIterations) {
        converged = true;
      }

      if (emptyClusterCount > 0) {
        m_NumClusters -= emptyClusterCount;
        if (converged) {
          Instances[] t = new Instances[m_NumClusters];
          int index = 0;
          for (int k = 0; k < tempI.length; k++) {
            if (tempI[k].numInstances() > 0) {
              t[index] = tempI[k];

              for (i = 0; i < tempI[k].numAttributes(); i++) {
                m_ClusterNominalCounts[index][i] = m_ClusterNominalCounts[k][i];
              }
              index++;
            }
          }
          tempI = t;
        } else {
          tempI = new Instances[m_NumClusters];
        }
      }

      if (!converged) {
        m_squaredErrors = new double[m_NumClusters];
        m_ClusterNominalCounts = new int[m_NumClusters][instances
          .numAttributes()][0];
      }
    }

    if (m_displayStdDevs) {
      m_ClusterStdDevs = new Instances(instances, m_NumClusters);
    }
    m_ClusterSizes = new int[m_NumClusters];
    for (i = 0; i < m_NumClusters; i++) {
      if (m_displayStdDevs) {
        double[] vals2 = new double[instances.numAttributes()];
        for (int j = 0; j < instances.numAttributes(); j++) {
          if (instances.attribute(j).isNumeric()) {
            vals2[j] = Math.sqrt(tempI[i].variance(j));
          } else {
            vals2[j] = Instance.missingValue();
          }
        }
        m_ClusterStdDevs.add(new Instance(1.0, vals2));
      }
      m_ClusterSizes[i] = tempI[i].numInstances();
    }

    // Save memory!!
    m_DistanceFunction.clean();
  }

  /**
   * Move the centroid to it's new coordinates. Generate the centroid
   * coordinates based on it's members (objects assigned to the cluster of the
   * centroid) and the distance function being used.
   * 
   * @param centroidIndex index of the centroid which the coordinates will be
   *          computed
   * @param members the objects that are assigned to the cluster of this
   *          centroid
   * @param updateClusterInfo if the method is supposed to update the m_Cluster
   *          arrays
   * @return the centroid coordinates
   */
  protected double[] moveCentroid(int centroidIndex, Instances members,
    boolean updateClusterInfo) {
    double[] vals = new double[members.numAttributes()];

    // used only for Manhattan Distance
    Instances sortedMembers = null;
    int middle = 0;
    boolean dataIsEven = false;

    if (m_DistanceFunction instanceof ManhattanDistance) {
      middle = (members.numInstances() - 1) / 2;
      dataIsEven = ((members.numInstances() % 2) == 0);
      if (m_PreserveOrder) {
        sortedMembers = members;
      } else {
        sortedMembers = new Instances(members);
      }
    }

    for (int j = 0; j < members.numAttributes(); j++) {

      // in case of Euclidian distance the centroid is the mean point
      // in case of Manhattan distance the centroid is the median point
      // in both cases, if the attribute is nominal, the centroid is the mode
      if (m_DistanceFunction instanceof EuclideanDistance
        || members.attribute(j).isNominal()) {
        vals[j] = members.meanOrMode(j);
      } else if (m_DistanceFunction instanceof ManhattanDistance) {
        // singleton special case
        if (members.numInstances() == 1) {
          vals[j] = members.instance(0).value(j);
        } else {
          vals[j] = sortedMembers.kthSmallestValue(j, middle + 1);
          if (dataIsEven) {
            vals[j] = (vals[j] + sortedMembers.kthSmallestValue(j, middle + 2)) / 2;
          }
        }
      }

      if (updateClusterInfo) {
        m_ClusterMissingCounts[centroidIndex][j] = members.attributeStats(j).missingCount;
        m_ClusterNominalCounts[centroidIndex][j] = members.attributeStats(j).nominalCounts;
        if (members.attribute(j).isNominal()) {
          if (m_ClusterMissingCounts[centroidIndex][j] > m_ClusterNominalCounts[centroidIndex][j][Utils
            .maxIndex(m_ClusterNominalCounts[centroidIndex][j])]) {
            vals[j] = Instance.missingValue(); // mark mode as missing
          }
        } else {
          if (m_ClusterMissingCounts[centroidIndex][j] == members
            .numInstances()) {
            vals[j] = Instance.missingValue(); // mark mean as missing
          }
        }
      }
    }
    if (updateClusterInfo) {
      m_ClusterCentroids.add(new Instance(1.0, vals));
    }
    return vals;
  }

  /**
   * clusters an instance that has been through the filters
   * 
   * @param instance the instance to assign a cluster to
   * @param updateErrors if true, update the within clusters sum of errors
   * @return a cluster number
   */
  private int clusterProcessedInstance(Instance instance, boolean updateErrors) {
    double minDist = Integer.MAX_VALUE;
    int bestCluster = 0;
    for (int i = 0; i < m_NumClusters; i++) {
      double dist = m_DistanceFunction.distance(instance,
        m_ClusterCentroids.instance(i));
      if (dist < minDist) {
        minDist = dist;
        bestCluster = i;
      }
    }
    if (updateErrors) {
      if (m_DistanceFunction instanceof EuclideanDistance) {
        // Euclidean distance to Squared Euclidean distance
        minDist *= minDist;
      }
      m_squaredErrors[bestCluster] += minDist;
    }
    return bestCluster;
  }

  /**
   * Classifies a given instance.
   * 
   * @param instance the instance to be assigned to a cluster
   * @return the number of the assigned cluster as an interger if the class is
   *         enumerated, otherwise the predicted value
   * @throws Exception if instance could not be classified successfully
   */
  
  public int clusterInstance(Instance instance) throws Exception {
    Instance inst = null;
    if (!m_dontReplaceMissing) {
      m_ReplaceMissingFilter.input(instance);
      m_ReplaceMissingFilter.batchFinished();
      inst = m_ReplaceMissingFilter.output();
    } else {
      inst = instance;
    }

    return clusterProcessedInstance(inst, false);
  }

  /**
   * Returns the number of clusters.
   * 
   * @return the number of clusters generated for a training dataset.
   * @throws Exception if number of clusters could not be returned successfully
   */
  
  public int numberOfClusters() throws Exception {
    return m_NumClusters;
  }

  /**
   * Returns an enumeration describing the available options.
   * 
   * @return an enumeration of all the available options.
   */
  
  public Enumeration listOptions() {
    Vector result = new Vector();

    result.addElement(new Option("\tnumber of clusters.\n" + "\t(default 2).",
      "N", 1, "-N <num>"));
    result.addElement(new Option("\tDisplay std. deviations for centroids.\n",
      "V", 0, "-V"));
    result.addElement(new Option(
      "\tDon't replace missing values with mean/mode.\n", "M", 0, "-M"));

    result.add(new Option("\tDistance function to use.\n"
      + "\t(default: weka.core.EuclideanDistance)", "A", 1,
      "-A <classname and options>"));

    result.add(new Option("\tMaximum number of iterations.\n", "I", 1,
      "-I <num>"));

    result.addElement(new Option("\tPreserve order of instances.\n", "O", 0,
      "-O"));

    Enumeration en = super.listOptions();
    while (en.hasMoreElements()) {
      result.addElement(en.nextElement());
    }

    return result.elements();
  }

  /**
   * Returns the tip text for this property
   * 
   * @return tip text for this property suitable for displaying in the
   *         explorer/experimenter gui
   */
  public String numClustersTipText() {
    return "set number of clusters";
  }

  /**
   * set the number of clusters to generate
   * 
   * @param n the number of clusters to generate
   * @throws Exception if number of clusters is negative
   */
  
  public void setNumClusters(int n) throws Exception {
    if (n <= 0) {
      throw new Exception("Number of clusters must be > 0");
    }
    m_NumClusters = n;
  }

  /**
   * gets the number of clusters to generate
   * 
   * @return the number of clusters to generate
   */
  public int getNumClusters() {
    return m_NumClusters;
  }

  /**
   * Returns the tip text for this property
   * 
   * @return tip text for this property suitable for displaying in the
   *         explorer/experimenter gui
   */
  public String maxIterationsTipText() {
    return "set maximum number of iterations";
  }

  /**
   * set the maximum number of iterations to be executed
   * 
   * @param n the maximum number of iterations
   * @throws Exception if maximum number of iteration is smaller than 1
   */
  public void setMaxIterations(int n) throws Exception {
    if (n <= 0) {
      throw new Exception("Maximum number of iterations must be > 0");
    }
    m_MaxIterations = n;
  }

  /**
   * gets the number of maximum iterations to be executed
   * 
   * @return the number of clusters to generate
   */
  public int getMaxIterations() {
    return m_MaxIterations;
  }

  /**
   * Returns the tip text for this property
   * 
   * @return tip text for this property suitable for displaying in the
   *         explorer/experimenter gui
   */
  public String displayStdDevsTipText() {
    return "Display std deviations of numeric attributes "
      + "and counts of nominal attributes.";
  }

  /**
   * Sets whether standard deviations and nominal count Should be displayed in
   * the clustering output
   * 
   * @param stdD true if std. devs and counts should be displayed
   */
  public void setDisplayStdDevs(boolean stdD) {
    m_displayStdDevs = stdD;
  }

  /**
   * Gets whether standard deviations and nominal count Should be displayed in
   * the clustering output
   * 
   * @return true if std. devs and counts should be displayed
   */
  public boolean getDisplayStdDevs() {
    return m_displayStdDevs;
  }

  /**
   * Returns the tip text for this property
   * 
   * @return tip text for this property suitable for displaying in the
   *         explorer/experimenter gui
   */
  public String dontReplaceMissingValuesTipText() {
    return "Replace missing values globally with mean/mode.";
  }

  /**
   * Sets whether missing values are to be replaced
   * 
   * @param r true if missing values are to be replaced
   */
  public void setDontReplaceMissingValues(boolean r) {
    m_dontReplaceMissing = r;
  }

  /**
   * Gets whether missing values are to be replaced
   * 
   * @return true if missing values are to be replaced
   */
  public boolean getDontReplaceMissingValues() {
    return m_dontReplaceMissing;
  }

  /**
   * Returns the tip text for this property.
   * 
   * @return tip text for this property suitable for displaying in the
   *         explorer/experimenter gui
   */
  public String distanceFunctionTipText() {
    return "The distance function to use for instances comparison "
      + "(default: weka.core.EuclideanDistance). ";
  }

  /**
   * returns the distance function currently in use.
   * 
   * @return the distance function
   */
  public DistanceFunction getDistanceFunction() {
    return m_DistanceFunction;
  }

  /**
   * sets the distance function to use for instance comparison.
   * 
   * @param df the new distance function to use
   * @throws Exception if instances cannot be processed
   */
  public void setDistanceFunction(DistanceFunction df) throws Exception {
    if (!(df instanceof EuclideanDistance)
      && !(df instanceof ManhattanDistance)) {
      throw new Exception(
        "SimpleKMeans currently only supports the Euclidean and Manhattan distances.");
    }
    m_DistanceFunction = df;
  }

  /**
   * Returns the tip text for this property
   * 
   * @return tip text for this property suitable for displaying in the
   *         explorer/experimenter gui
   */
  public String preserveInstancesOrderTipText() {
    return "Preserve order of instances.";
  }

  /**
   * Sets whether order of instances must be preserved
   * 
   * @param r true if missing values are to be replaced
   */
  public void setPreserveInstancesOrder(boolean r) {
    m_PreserveOrder = r;
  }

  /**
   * Gets whether order of instances must be preserved
   * 
   * @return true if missing values are to be replaced
   */
  public boolean getPreserveInstancesOrder() {
    return m_PreserveOrder;
  }

  /**
   * Parses a given list of options.
   * <p/>
   * 
   * <!-- options-start --> Valid options are:
   * <p/>
   * 
   * <pre>
   * -N &lt;num&gt;
   *  number of clusters.
   *  (default 2).
   * </pre>
   * 
   * <pre>
   * -V
   *  Display std. deviations for centroids.
   * </pre>
   * 
   * <pre>
   * -M
   *  Replace missing values with mean/mode.
   * </pre>
   * 
   * <pre>
   * -S &lt;num&gt;
   *  Random number seed.
   *  (default 10)
   * </pre>
   * 
   * <pre>
   * -A &lt;classname and options&gt;
   *  Distance function to be used for instance comparison
   *  (default weka.core.EuclidianDistance)
   * </pre>
   * 
   * <pre>
   * -I &lt;num&gt;
   *  Maximum number of iterations.
   * </pre>
   * 
   * <pre>
   * -O
   *  Preserve order of instances.
   * </pre>
   * 
   * <!-- options-end -->
   * 
   * @param options the list of options as an array of strings
   * @throws Exception if an option is not supported
   */
  
  public void setOptions(String[] options) throws Exception {

    m_displayStdDevs = Utils.getFlag("V", options);
    m_dontReplaceMissing = Utils.getFlag("M", options);

    String optionString = Utils.getOption('N', options);

    if (optionString.length() != 0) {
      setNumClusters(Integer.parseInt(optionString));
    }

    optionString = Utils.getOption("I", options);
    if (optionString.length() != 0) {
      setMaxIterations(Integer.parseInt(optionString));
    }

    String distFunctionClass = Utils.getOption('A', options);
    if (distFunctionClass.length() != 0) {
      String distFunctionClassSpec[] = Utils.splitOptions(distFunctionClass);
      if (distFunctionClassSpec.length == 0) {
        throw new Exception("Invalid DistanceFunction specification string.");
      }
      String className = distFunctionClassSpec[0];
      distFunctionClassSpec[0] = "";

      setDistanceFunction((DistanceFunction) Utils.forName(
        DistanceFunction.class, className, distFunctionClassSpec));
    } else {
      setDistanceFunction(new EuclideanDistance());
    }

    m_PreserveOrder = Utils.getFlag("O", options);

    super.setOptions(options);
  }

  /**
   * Gets the current settings of SimpleKMeans
   * 
   * @return an array of strings suitable for passing to setOptions()
   */
  
  public String[] getOptions() {
    int i;
    Vector result;
    String[] options;

    result = new Vector();

    if (m_displayStdDevs) {
      result.add("-V");
    }

    if (m_dontReplaceMissing) {
      result.add("-M");
    }

    result.add("-N");
    result.add("" + getNumClusters());

    result.add("-A");
    result.add((m_DistanceFunction.getClass().getName() + " " + Utils
      .joinOptions(m_DistanceFunction.getOptions())).trim());

    result.add("-I");
    result.add("" + getMaxIterations());

    if (m_PreserveOrder) {
      result.add("-O");
    }

    options = super.getOptions();
    for (i = 0; i < options.length; i++) {
      result.add(options[i]);
    }

    return (String[]) result.toArray(new String[result.size()]);
  }

  /**
   * return a string describing this clusterer
   * 
   * @return a description of the clusterer as a string
   */
  
  public String toString() {
    if (m_ClusterCentroids == null) {
      return "No clusterer built yet!";
    }

    int maxWidth = 0;
    int maxAttWidth = 0;
    boolean containsNumeric = false;
    for (int i = 0; i < m_NumClusters; i++) {
      for (int j = 0; j < m_ClusterCentroids.numAttributes(); j++) {
        if (m_ClusterCentroids.attribute(j).name().length() > maxAttWidth) {
          maxAttWidth = m_ClusterCentroids.attribute(j).name().length();
        }
        if (m_ClusterCentroids.attribute(j).isNumeric()) {
          containsNumeric = true;
          double width = Math.log(Math.abs(m_ClusterCentroids.instance(i)
            .value(j))) / Math.log(10.0);
          // System.err.println(m_ClusterCentroids.instance(i).value(j)+" "+width);
          if (width < 0) {
            width = 1;
          }
          // decimal + # decimal places + 1
          width += 6.0;
          if ((int) width > maxWidth) {
            maxWidth = (int) width;
          }
        }
      }
    }

    for (int i = 0; i < m_ClusterCentroids.numAttributes(); i++) {
      if (m_ClusterCentroids.attribute(i).isNominal()) {
        Attribute a = m_ClusterCentroids.attribute(i);
        for (int j = 0; j < m_ClusterCentroids.numInstances(); j++) {
          String val = a.value((int) m_ClusterCentroids.instance(j).value(i));
          if (val.length() > maxWidth) {
            maxWidth = val.length();
          }
        }
        for (int j = 0; j < a.numValues(); j++) {
          String val = a.value(j) + " ";
          if (val.length() > maxAttWidth) {
            maxAttWidth = val.length();
          }
        }
      }
    }

    if (m_displayStdDevs) {
      // check for maximum width of maximum frequency count
      for (int i = 0; i < m_ClusterCentroids.numAttributes(); i++) {
        if (m_ClusterCentroids.attribute(i).isNominal()) {
          int maxV = Utils.maxIndex(m_FullNominalCounts[i]);
          /*
           * int percent = (int)((double)m_FullNominalCounts[i][maxV] /
           * Utils.sum(m_ClusterSizes) * 100.0);
           */
          int percent = 6; // max percent width (100%)
          String nomV = "" + m_FullNominalCounts[i][maxV];
          // + " (" + percent + "%)";
          if (nomV.length() + percent > maxWidth) {
            maxWidth = nomV.length() + 1;
          }
        }
      }
    }

    // check for size of cluster sizes
    for (int m_ClusterSize : m_ClusterSizes) {
      String size = "(" + m_ClusterSize + ")";
      if (size.length() > maxWidth) {
        maxWidth = size.length();
      }
    }

    if (m_displayStdDevs && maxAttWidth < "missing".length()) {
      maxAttWidth = "missing".length();
    }

    String plusMinus = "+/-";
    maxAttWidth += 2;
    if (m_displayStdDevs && containsNumeric) {
      maxWidth += plusMinus.length();
    }
    if (maxAttWidth < "Attribute".length() + 2) {
      maxAttWidth = "Attribute".length() + 2;
    }

    if (maxWidth < "Full Data".length()) {
      maxWidth = "Full Data".length() + 1;
    }

    if (maxWidth < "missing".length()) {
      maxWidth = "missing".length() + 1;
    }

    StringBuffer temp = new StringBuffer();
    // String naString = "N/A";

    /*
     * for (int i = 0; i < maxWidth+2; i++) { naString += " "; }
     */
    temp.append("\nkMeans\n======\n");
    temp.append("\nNumber of iterations: " + m_Iterations + "\n");

    if (m_DistanceFunction instanceof EuclideanDistance) {
      temp.append("Within cluster sum of squared errors: "
        + Utils.sum(m_squaredErrors));
    } else {
      temp.append("Sum of within cluster distances: "
        + Utils.sum(m_squaredErrors));
    }

    if (!m_dontReplaceMissing) {
      temp.append("\nMissing values globally replaced with mean/mode");
    }

    temp.append("\n\nCluster centroids:\n");
    temp.append(pad("Cluster#", " ", (maxAttWidth + (maxWidth * 2 + 2))
      - "Cluster#".length(), true));

    temp.append("\n");
    temp
      .append(pad("Attribute", " ", maxAttWidth - "Attribute".length(), false));

    temp
      .append(pad("Full Data", " ", maxWidth + 1 - "Full Data".length(), true));

    // cluster numbers
    for (int i = 0; i < m_NumClusters; i++) {
      String clustNum = "" + i;
      temp.append(pad(clustNum, " ", maxWidth + 1 - clustNum.length(), true));
    }
    temp.append("\n");

    // cluster sizes
    String cSize = "(" + Utils.sum(m_ClusterSizes) + ")";
    temp.append(pad(cSize, " ", maxAttWidth + maxWidth + 1 - cSize.length(),
      true));
    for (int i = 0; i < m_NumClusters; i++) {
      cSize = "(" + m_ClusterSizes[i] + ")";
      temp.append(pad(cSize, " ", maxWidth + 1 - cSize.length(), true));
    }
    temp.append("\n");

    temp.append(pad("", "=",
      maxAttWidth
        + (maxWidth * (m_ClusterCentroids.numInstances() + 1)
          + m_ClusterCentroids.numInstances() + 1), true));
    temp.append("\n");

    for (int i = 0; i < m_ClusterCentroids.numAttributes(); i++) {
      String attName = m_ClusterCentroids.attribute(i).name();
      temp.append(attName);
      for (int j = 0; j < maxAttWidth - attName.length(); j++) {
        temp.append(" ");
      }

      String strVal;
      String valMeanMode;
      // full data
      if (m_ClusterCentroids.attribute(i).isNominal()) {
        if (m_FullMeansOrMediansOrModes[i] == -1) { // missing
          valMeanMode = pad("missing", " ", maxWidth + 1 - "missing".length(),
            true);
        } else {
          valMeanMode = pad(
            (strVal = m_ClusterCentroids.attribute(i).value(
              (int) m_FullMeansOrMediansOrModes[i])), " ", maxWidth + 1
              - strVal.length(), true);
        }
      } else {
        if (Double.isNaN(m_FullMeansOrMediansOrModes[i])) {
          valMeanMode = pad("missing", " ", maxWidth + 1 - "missing".length(),
            true);
        } else {
          valMeanMode = pad(
            (strVal = Utils.doubleToString(m_FullMeansOrMediansOrModes[i],
              maxWidth, 4).trim()), " ", maxWidth + 1 - strVal.length(), true);
        }
      }
      temp.append(valMeanMode);

      for (int j = 0; j < m_NumClusters; j++) {
        if (m_ClusterCentroids.attribute(i).isNominal()) {
          if (m_ClusterCentroids.instance(j).isMissing(i)) {
            valMeanMode = pad("missing", " ",
              maxWidth + 1 - "missing".length(), true);
          } else {
            valMeanMode = pad(
              (strVal = m_ClusterCentroids.attribute(i).value(
                (int) m_ClusterCentroids.instance(j).value(i))), " ", maxWidth
                + 1 - strVal.length(), true);
          }
        } else {
          if (m_ClusterCentroids.instance(j).isMissing(i)) {
            valMeanMode = pad("missing", " ",
              maxWidth + 1 - "missing".length(), true);
          } else {
            valMeanMode = pad(
              (strVal = Utils.doubleToString(
                m_ClusterCentroids.instance(j).value(i), maxWidth, 4).trim()),
              " ", maxWidth + 1 - strVal.length(), true);
          }
        }
        temp.append(valMeanMode);
      }
      temp.append("\n");

      if (m_displayStdDevs) {
        // Std devs/max nominal
        String stdDevVal = "";

        if (m_ClusterCentroids.attribute(i).isNominal()) {
          // Do the values of the nominal attribute
          Attribute a = m_ClusterCentroids.attribute(i);
          for (int j = 0; j < a.numValues(); j++) {
            // full data
            String val = "  " + a.value(j);
            temp.append(pad(val, " ", maxAttWidth + 1 - val.length(), false));
            int count = m_FullNominalCounts[i][j];
            int percent = (int) ((double) m_FullNominalCounts[i][j]
              / Utils.sum(m_ClusterSizes) * 100.0);
            String percentS = "" + percent + "%)";
            percentS = pad(percentS, " ", 5 - percentS.length(), true);
            stdDevVal = "" + count + " (" + percentS;
            stdDevVal = pad(stdDevVal, " ", maxWidth + 1 - stdDevVal.length(),
              true);
            temp.append(stdDevVal);

            // Clusters
            for (int k = 0; k < m_NumClusters; k++) {
              count = m_ClusterNominalCounts[k][i][j];
              percent = (int) ((double) m_ClusterNominalCounts[k][i][j]
                / m_ClusterSizes[k] * 100.0);
              percentS = "" + percent + "%)";
              percentS = pad(percentS, " ", 5 - percentS.length(), true);
              stdDevVal = "" + count + " (" + percentS;
              stdDevVal = pad(stdDevVal, " ",
                maxWidth + 1 - stdDevVal.length(), true);
              temp.append(stdDevVal);
            }
            temp.append("\n");
          }
          // missing (if any)
          if (m_FullMissingCounts[i] > 0) {
            // Full data
            temp.append(pad("  missing", " ",
              maxAttWidth + 1 - "  missing".length(), false));
            int count = m_FullMissingCounts[i];
            int percent = (int) ((double) m_FullMissingCounts[i]
              / Utils.sum(m_ClusterSizes) * 100.0);
            String percentS = "" + percent + "%)";
            percentS = pad(percentS, " ", 5 - percentS.length(), true);
            stdDevVal = "" + count + " (" + percentS;
            stdDevVal = pad(stdDevVal, " ", maxWidth + 1 - stdDevVal.length(),
              true);
            temp.append(stdDevVal);

            // Clusters
            for (int k = 0; k < m_NumClusters; k++) {
              count = m_ClusterMissingCounts[k][i];
              percent = (int) ((double) m_ClusterMissingCounts[k][i]
                / m_ClusterSizes[k] * 100.0);
              percentS = "" + percent + "%)";
              percentS = pad(percentS, " ", 5 - percentS.length(), true);
              stdDevVal = "" + count + " (" + percentS;
              stdDevVal = pad(stdDevVal, " ",
                maxWidth + 1 - stdDevVal.length(), true);
              temp.append(stdDevVal);
            }

            temp.append("\n");
          }

          temp.append("\n");
        } else {
          // Full data
          if (Double.isNaN(m_FullMeansOrMediansOrModes[i])) {
            stdDevVal = pad("--", " ", maxAttWidth + maxWidth + 1 - 2, true);
          } else {
            stdDevVal = pad(
              (strVal = plusMinus
                + Utils.doubleToString(m_FullStdDevs[i], maxWidth, 4).trim()),
              " ", maxWidth + maxAttWidth + 1 - strVal.length(), true);
          }
          temp.append(stdDevVal);

          // Clusters
          for (int j = 0; j < m_NumClusters; j++) {
            if (m_ClusterCentroids.instance(j).isMissing(i)) {
              stdDevVal = pad("--", " ", maxWidth + 1 - 2, true);
            } else {
              stdDevVal = pad(
                (strVal = plusMinus
                  + Utils.doubleToString(m_ClusterStdDevs.instance(j).value(i),
                    maxWidth, 4).trim()), " ", maxWidth + 1 - strVal.length(),
                true);
            }
            temp.append(stdDevVal);
          }
          temp.append("\n\n");
        }
      }
    }

    temp.append("\n\n");
    return temp.toString();
  }

  private String pad(String source, String padChar, int length, boolean leftPad) {
    StringBuffer temp = new StringBuffer();

    if (leftPad) {
      for (int i = 0; i < length; i++) {
        temp.append(padChar);
      }
      temp.append(source);
    } else {
      temp.append(source);
      for (int i = 0; i < length; i++) {
        temp.append(padChar);
      }
    }
    return temp.toString();
  }

  /**
   * Gets the the cluster centroids
   * 
   * @return the cluster centroids
   */
  public Instances getClusterCentroids() {
    return m_ClusterCentroids;
  }

  /**
   * Gets the standard deviations of the numeric attributes in each cluster
   * 
   * @return the standard deviations of the numeric attributes in each cluster
   */
  public Instances getClusterStandardDevs() {
    return m_ClusterStdDevs;
  }

  /**
   * Returns for each cluster the frequency counts for the values of each
   * nominal attribute
   * 
   * @return the counts
   */
  public int[][][] getClusterNominalCounts() {
    return m_ClusterNominalCounts;
  }

  /**
   * Gets the squared error for all clusters
   * 
   * @return the squared error
   */
  public double getSquaredError() {
    return Utils.sum(m_squaredErrors);
  }

  /**
   * Gets the number of instances in each cluster
   * 
   * @return The number of instances in each cluster
   */
  public int[] getClusterSizes() {
    return m_ClusterSizes;
  }

  /**
   * Gets the assignments for each instance
   * 
   * @return Array of indexes of the centroid assigned to each instance
   * @throws Exception if order of instances wasn't preserved or no assignments
   *           were made
   */
  public int[] getAssignments() throws Exception {
    if (!m_PreserveOrder) {
      throw new Exception(
        "The assignments are only available when order of instances is preserved (-O)");
    }
    if (m_Assignments == null) {
      throw new Exception("No assignments made.");
    }
    return m_Assignments;
  }

  /**
   * Returns the revision string.
   * 
   * @return the revision
   */
  
  public String getRevision() {
    return RevisionUtils.extract("$Revision: 10537 $");
  }

  /**
   * Main method for testing this class.
   * 
   * @param argv should contain the following arguments:
   *          <p>
   *          -t training file [-N number of clusters]
   */
  public static void main(String[] argv) {
    runClusterer(new SimpleKMeans(), argv);
  }
}
